Browsing by Author "Debnath, Susanta Chandra"

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Human Behaviour Impact to Use of Smartphones with the Python Implementation Using Naive Bayesian
(11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020, IEEE, 2020-10-15) Talha, Iftakhar Mohammad; Salehin, Imrus; Debnath, Susanta Chandra; Saifuzzaman, Mohd.; Moon, Nazmun Nessa; Nur, Fernaz Narin
A change of behavior in special groups and many sustainable smart populations increasing day by day for excessive uses of smartphones. In recent years, the use of smartphones and mental imbalances have become a major problem with increasing negative effects. In our study, we find out the major problem of the negative side and its different sources like mental imbalance, stress, depression, loneliness, etc. Bayes' theorem and classifier, support vector machine, special data set of human behavior, and probability are used to calculate accuracy. For collecting data from three major sections, we use the physical methods, virtual methods, and medical reports. So, a vast data set is trained by data to compare method, and also probability is used for predicting the validity of the data model. Naive Bayes' theorem accurate 71% positive which is indicated the negative impact of human behavior. Based on the SVM classifier, we separate the barrier between the impact of positive and negative data. In SVM, we set up a parameter to measure negative and positive values. Python library function is a major component to calculate all instructions and also use for data training. Finally, we compare the results obtained by our proposed specialization with the results obtained from the three baseline landmarks.
Multilabel Movie Genre Classification from Movie Subtitle Using Supervised and Unsupervised Machine Learning Approach
(Daffodil International University, 2021-06-02) Hasan, Md. Mehedi; Debnath, Susanta Chandra; Hasan, Md. Mozahid
Technological breakthroughs and the interest of business entities have made the categorization of media products increasingly conventional in this digital environment. This is usually often a multilabel scenario in which an object might be labeled with several categories. Most of the literature addresses the movie genre classification as a mono-labeling task, generally based on audio-visual features. This study addressed a multilabel movie genre classification model using both supervised and unsupervised machine learning techniques to classify the movies into their corresponding genres. We created a dataset consisting of English subtitle files taken from The Movie Database (IMDB), which contains 1200 movies and each of the movies was labeled according to a set of eleven genre labels. We experimented with two feature extraction methods combined with the classifiers and a feature selection technique to reduce the dimensionality of our proposed work. In this study, we compared the performance of unsupervised and supervised techniques for the classification using several standard performance measures using both feature representation methods. We assessed that the best performers of the unsupervised techniques are K-means and Bisecting k-means in the term of cluster quality. In contrast, we observed the model evaluation using KNN, SVM and DT and find that SVM is better than the other classifiers among the supervised techniques. Finally, we compared the unsupervised and supervised technique in the term of quality of the clusters. We observed that the K-Means and Bisecting K-Means of unsupervised technique produced the cluster of higher quality than the SVM, DT and KNN supervised technique. We addressed the reason for the outliers of the training set and recommended to use unsupervised techniques to improve the assignment of predefining the categories and labeling the textual documents in the training set.
Natural Language Processing Based Advanced Method of Unnecessary Video Detection
(International Journal of Electrical and Computer Engineering, 2021) Moon, Nazmun Nessa; Salehin, Imrus; Parvin, Masuma; Hasan, Md. Mehedi; Talha, Iftakhar Mohammad; Debnath, Susanta Chandra; Nur, Fernaz Narin; Saifuzzaman, Mohd.
In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare the data set. We use both Naive Bayes and logistic regression classification algorithms in this detection system to determine the best accuracy for our system. In our research, our video MP4 data has converted to plain text data using the python advance library function. This brief study discusses the identification of unauthorized, unsocial, unnecessary, unfinished, and malicious videos when using oral video record data. By analyzing our data sets through this advanced model, we can decide which videos should be accepted or rejected for the further actions.